2021-04-132021-04-132021-04-09https://repositorio.redinvestigadores.org/handle/Riec/100By interpreting exporters’ dynamics as a complex learning process, this paper constitutes the first attempt to investigate the effectiveness of different Machine Learning (ML) techniques in predicting firms’ trade status. We focus on the probability of Colombian firms surviving in the export market under two different scenarios: a COVID-19 setting and a non-COVID-19 counterfactual situation. By comparing the resulting predictions, we estimate the individual treatment effect of the COVID-19 shock on firms’ outcomes. Finally, we use recursive partitioning methods to identify subgroups with differential treatment effects. We find that, besides the temporal dimension, the main factors predicting treatment heterogeneity are interactions between firm size and industry.By interpreting exporters’ dynamics as a complex learning process, this paper constitutes the first attempt to investigate the effectiveness of different Machine Learning (ML) techniques in predicting firms’ trade status. We focus on the probability of Colombian firms surviving in the export market under two different scenarios: a COVID-19 setting and a non-COVID-19 counterfactual situation. By comparing the resulting predictions, we estimate the individual treatment effect of the COVID-19 shock on firms’ outcomes. Finally, we use recursive partitioning methods to identify subgroups with differential treatment effects. We find that, besides the temporal dimension, the main factors predicting treatment heterogeneity are interactions between firm size and industry.31 páginasPDFengOpen AccessWorking paperF14 - Empirical Studies of TradeF17 - Trade Forecasting and SimulationD22 - Firm Behavior: Empirical AnalysisL25 - Firm Performance: Size, Diversification, and ScopeMachine LearningInternational TradeCOVID-19Impacto económico -- Aislamiento preventivo -- Covid 19 -- ColombiaMachine Learning -- Probabilidades -- Estudios comparados -- ColombiaAcceso abiertoAtribucion-NoComercial-CompartirIgual CC BY-NC-SA 4.0